from joblib import load
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import tensorflow as tf

class timing(object):
    def __init__(self):
        self.loaded_model = tf.keras.models.load_model('fyy7/model.keras')
        self.scaler = load('fyy7/scaler.joblib')

    def get_hourly_trend(self):
        """
        获取模型预测结果
        """
        n_steps = 7
        df_pivot = pd.read_csv('fyy7/scenic_data.csv')
        # latest_data = df_pivot.iloc[-n_steps:].values
        x_values =df_pivot.iloc[-n_steps:]
        x_values.iloc[-1,x_values.columns.get_loc('count')]=0
        x_lasterst=x_values.values
        latest_data = x_lasterst.reshape(1, n_steps,x_lasterst.shape[1])

        predicted = self.loaded_model.predict(latest_data)
        predicted_count = self.scaler.inverse_transform(predicted)  # 反归一化
        predicted_count[predicted_count < 0] = 0
        hourly_trend = predicted_count[0].astype(int).tolist()
        print(hourly_trend)

if __name__ == '__main__':
    timing = timing()
    timing.get_hourly_trend()